Systems and methods for detecting tremors

ABSTRACT

In one embodiment, a system for detecting tremors includes a contactless tremor detector, the detector including an oscillator circuit having a sensing coil, an oscillator configured to generate alternating electromagnetic fields, and a frequency counter configured to sense the resonant frequency of the oscillator circuit as the resonant frequency changes in response to movement of a body part of an individual adjacent to the sensing coil, wherein the system is configured to determine a frequency of movement of the body part from the sensed resonant frequency for the purpose of determining if the individual is experiencing tremors.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to co-pending U.S. ProvisionalApplication Ser. No. 62/221,395, filed Sep. 21, 2015, which is herebyincorporated by reference herein in its entirety.

BACKGROUND

There are currently no standard diagnostic tools in clinical practicethat can be used to quantitatively assess tremors, such as those causedby Parkinson's disease or fatigue. In current practice, symptoms areobserved and evaluated with clinical protocols, but the results aresubjective and can differ from person to person.

The frequency distribution of tremor syndromes has been investigatedbased on etiology. Voluntary hand movements range between 0 and 2 Hzwhile pathological tremors range between 3 and 12 Hz. Although detectingpathological tremors may help early diagnosis of diseases, such asParkinson's disease, the frequency range of such tremors makes itdifficult to observe tremors with the eyes.

Recently, research has been conducted using accelerometers with the goalof detecting the frequencies of tremors. For example, handheld deviceshaving embedded accelerometers, such as smart phones, have been proposedto monitor hand tremors.

These devices, however, present an additional load, physically orsubconsciously, to the patients who may not completely relax theirmuscles. This makes it more difficult to detect the tremors. Aside fromthat, the amplitude of the hand tremor cannot be accurately detectedusing an accelerometer, which detects the acceleration of an objectinstead of its motion.

From the above discussion, it can be appreciated that it would bedesirable to have a system and method for accurately detecting tremors.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure may be better understood with reference to thefollowing figures. Matching reference numerals designate correspondingparts throughout the figures, which are not necessarily drawn to scale.

FIG. 1 is a schematic diagram of an embodiment of a system for detectingtremors.

FIG. 2 is an equivalent circuit diagram of an oscillation circuit shownin FIG. 1.

FIG. 3 is a schematic diagram of a simulation model of the oscillationcircuit shown in FIG. 1.

FIG. 4 is a graph that shows resonant frequencies at various distances,D, between a hand and a sensing coil of a tremor detector.

FIG. 5 is a graph that identifies distance as a function of the resonantfrequency of an oscillation circuit of a tremor detectors.

FIG. 6 is a schematic diagram of a testing apparatus used to manipulatea wooden hand.

FIG. 7A is a graph that shows distance as a function of time when thewooden hand of FIG. 6 was actuated at 5 Hz.

FIG. 7B is a graph that shows the spectral distribution of the distancesof FIG. 7A.

FIG. 8A is a graph that shows tremor acceleration at x axis as afunction of time when the wooden hand was actuated at 5 Hz.

FIG. 8B is a graph that shows spectral distributions of theaccelerations of FIG. 8A.

FIG. 9A is a graph that shows tremor acceleration at y axis as afunction of time when the wooden hand was actuated at 5 Hz.

FIG. 9B is a graph that shows spectral distributions of theaccelerations of FIG. 9A.

FIG. 10A is a graph that shows tremor acceleration at z axis as afunction of time when the wooden hand was actuated at 5 Hz.

FIG. 10B is a graph that shows spectral distributions of theaccelerations of FIG. 10A.

DETAILED DESCRIPTION

As described above, it would be desirable to have a system and methodfor accurately detecting tremors. Disclosed herein are examples of suchsystems and methods. In some embodiments, a tremor detection systemcomprises a contactless tremor detector that includes an oscillatorcircuit. The oscillator circuit includes a sensing coil next to which apatient can place his or her hand (or other body part). The oscillatorcircuit generates alternating electromagnetic fields that generate aneddy current density on the surface of the user's hand. The magneticfields generated by the eddy current couple back to the sensing coil andchange the resonant frequency of the circuit. The changing resonantfrequency can then be used to determine the distance of the hand fromthe coil as a function of time, which can then be converted into afrequency that can provide an indication of the presence of tremor.

In the following disclosure, various specific embodiments are described.It is to be understood that those embodiments are exampleimplementations of the disclosed inventions and that alternativeembodiments are possible. All such embodiments are intended to fallwithin the scope of this disclosure.

FIG. 1 illustrates an embodiment of a tremor detection system 10. Asshown in this figure, the system 10 generally comprises a contactlesstremor detector 12 and a computing device 14 that is in electricalcommunication with the detector. In the embodiment of FIG. 1, the tremordetector 12 includes an oscillator circuit 16 having a sensing coil 18,an oscillator 22 connected to the coil, and a frequency counter 24connected to the oscillator. The sensing coil 18 can comprise a spiralcoil of conductive wire, such as copper wire. By way of example, thespiral coil can comprise approximately 50 turns of wire, an outerdiameter of approximately 12 cm, and a measured inductance ofapproximately 110 μH.

In some embodiments, the oscillator 22 and the frequency counter 24 canbe implemented as an integrated inductive sensor, such as an inductivesensing chip, which can be controlled by the microcontroller 26. In suchcases, the inductive sensing chip can sense inductance and convert itinto a digital signal that can be transmitted to the computing device 14by the microcontroller 26. By way of example, the inductive sensing chipcan comprise an LDC1000 and the microcontroller 26 can comprise anMSP430F5529, both of which are produced by Texas Instruments, Inc. Insome embodiments, the oscillator circuit 16 can have a total capacitanceof approximately 93 pF.

With further reference to FIG. 1, the computing device 14 comprises aprocessing device 28 and memory 30 (a non-transitory computer-readablemedium) that stores a tremor evaluation program 32 (i.e., logic and/orcomputer-implementable instructions) that can receive the frequencycounter data from the microcontroller 26 and generate information for anend user (e.g., physician) that can be used to diagnose a patientcondition.

As shown in FIG. 1, a patient (or other user) can place his or her hand(or other body part to be evaluated) in proximity to the sensing coil 18when an evaluation is to be performed. During operation of the system10, the oscillator 22 generates oscillating electromagnetic fields thatgenerate an eddy current density on the surface of a patient's hand whenit is placed in proximity to the sensing coil 18. The magnetic fieldsgenerated by the eddy current are then coupled back to the sensing coil18 and produce a difference current that changes the resonant frequencyof the oscillator circuit 16. Generally speaking, the resonant frequencyof the oscillator circuit 16 increases when the hand is brought closerto the sensing coil 18 and decreases when the hand is moved away fromthe coil. The resonant frequency changes are sensed by the frequencycounter 24 and converted into digital frequency counter data that isprovided to the microcontroller 26, which transmits them to thecomputing device 14.

In some embodiments, computing device 14 can correlate the frequencycounter data into distances that vary with time. By way of example, thefrequency counter data can be correlated to distances by an algorithm ofthe tremor evaluation program 32 using a correlation graph or table thatis constructed during a calibration process in which the resonantfrequency of the oscillation circuit 16 is measured as an object isplaced distances from the sensing coil 18. FIG. 5 shows an example graphthat correlates resonant frequency with distance. Through this process,the distance of the hand from the sensing coil 18 can be determined as afunction of time (a temporal domain signal). This distance data in thetemporal domain can then be converted into frequency data in thefrequency domain that provide an indication of the frequency of anymovement of the hand and, therefore, the frequency of any tremors thatare being produced. In some embodiments, the distance data can beconverted into frequency data by an algorithm of the tremor evaluationprogram 32 by performing a Fourier transform. FIG. 7B shows an examplegraph of hand movement frequencies obtained in this manner from distancedata shown in FIG. 7A. The movement frequency data can then be evaluatedby a physician for the purposes of diagnosing a condition of thepatient. In particular, the physician can identify the frequency bandthat has the greatest amount of signal (power density). If this bandfalls within a frequency band associated with a particular condition,such as Parkinson's disease, the presence of the condition is indicated.

FIG. 2 shows an equivalent circuit diagram for the oscillator circuit 16shown in FIG. 1. In FIG. 2, L1 is the inductance of the sensing coil 18,C1 is the tuning capacitance of the coil, and R1 is the resistance ofthe coil. L2 is the inductance induced by the hand and R2 is the handsurface resistance. When the oscillator circuit 16 generates analternating electromagnetic field, the eddy current density, J, isgenerated by the magnetic field on the hand. A magnetic field induced bythe eddy current is then coupled back to the coil and produces acurrent, which changes the resonant frequency.

FIG. 3 shows a simulation model for the oscillation circuit 16. Theequivalent impendence can be expressed as

$\begin{matrix}{{Z = {\frac{\frac{R_{1}}{\omega^{2}C_{1}^{2}} + {j\left( {\frac{L}{\omega\; C_{1}^{2}} - \frac{R_{1}^{2}}{\omega\; C_{1}} - \frac{\omega\; L^{2}}{C_{1}}} \right)}}{{R\frac{2}{1}} + \left( {{\omega\; L} - \frac{1}{\omega\; C_{1}}} \right)}\mspace{14mu}{where}}}{{L = {L_{1} - {L_{2}\frac{\omega^{2}M^{2}}{{R\frac{2}{2}} + {\omega^{2}L_{2}^{2}}}}}},}} & (1)\end{matrix}$ω is oscillator frequency, and M is the mutual inductance between thesensor coil and the human hand. In the simulation, C1=93 pF, L1=110 μH,and R1=0.165 Ω. According to the IEEE Standard 80, the internalresistance of the body is approximately as 300 Ω. An assumption was madethat the internal resistance of human hand is R2=50 Ω and the inductanceof the human hand is L2=20 μH. The working range between the hand andthe detector was 3.5 to 10 cm.

By the Bio-Savart law, the mutual inductance M is proportional to 1/D³where D is the distance. When the mutual inductance increases, theresonant frequency in the inductive circuit increases. The impedance ofthe equivalent circuit is shown in FIG. 4.

The relationship between the distance and the resonant frequency isshown in FIG. 5. With the detected resonant frequency, the distance canbe obtained. Through continuous counting of the resonant frequencies,the small variations between the coil and the hand can be dynamicallyobtained.

Experimental apparatus was designed to verify the theory. As shown inFIG. 6, the apparatus consisted of a wooden hand wrapped in cooper foilto emulate a human hand as it is difficult to control the tremorfrequency and magnitude of a real human hand. A solenoid actuator with apower requirement of 12 V and 1 A was used to actuate the wooden handwith a MOSFET (AO3414, 20 V, 4.2 A, N-Channel) to drive the actuator. AnMSP430 microcontroller and a 12 V power supply were used for control. Tomimic the movement of a real hand, the finger joints on the wooden handmodel were loosened and were free to move when the hand is driven by theactuator. The swing magnitude of the wooden hand was approximately 2 cm.The wooden hand was placed in front of the sensing coil. Variousactuation signals were tested.

When the wooden hand was placed 5 cm away from the sensing coil and themicrocontroller produced 5 Hz signals to drive the wooden hand,continuous frequency counts at 10,000 samples/s and 24-bit resolutionwere acquired by the tremor detector. The frequencies were converted toa temporal plot in term of distance shown in FIG. 7A. The spectraldistribution is shown in FIG. 7B. The motions from fingers contributedthe spreading of the spectral distribution.

A triaxial accelerometer, which was configured on an eZ430-Chronoswireless wearable device (Texas Instruments, Inc.), was used to recordthe tremor accelerations of the wooden hand for purposes of comparison.The wearable device included a triaxial accelerometer (Bosch SensortecBMA250), an RF transceiver (CC1101), and a microcontroller(MSP430F5509). The digital resolution of the triaxial accelerometer was10 bits with a measurement range of ±16 g, a sensitivity of 16 LSB/g,and a zero-g offset of ±80 mg. The accelerometer sample rate was 33samples/s for each axis.

The device was attached on the palm of wooden hand. Accelerations of thehand were recorded when the hand was driven by the 5 Hz signal. Threesets of acceleration data are shown in FIGS. 8, 9, and 10. Comparing thespectral distributions in FIGS. 8B, 9B, and 10B with the one measured bythe tremor detector, shown in FIG. 7B, only the signals from two axescan indicate the tremor signals. However, it is obvious that moreharmonic elements were recorded. The temporal waveforms also showeddistortion even though the hand model was moved with a single drivingfrequency. One reason for this is the low sample rate of the wearabledevice. However the main problem was that the accelerometers aredesigned to detect acceleration of an object, while the disclosed tremordetector detects the distance variations. This comparison demonstratesthat the disclosed tremor sensor is better suited for detecting handtremors when the subject consciously maintains his or her arm still suchthat the acceleration is not significant enough to be accuratelydetected.

The disclosed systems and methods can be used to quantify tremorsassociated with various diseases, such as fundamental tremors,Parkinson's disease, multiple sclerosis, stroke, traumatic brain injury,chronic kidney disease, and neurodegenerative diseases. In addition,tremors associated with other conditions or circumstances, such asanxiety, fear, fatigue from exercise, or the use or withdraw of drugs(such as amphetamines, cocaine, caffeine, corticosteroids, SSRI) andalcohol, can be detected. While the systems and methods have beendescribed as being used to quantify hand tremors, it is noted that anybody tremors can be measured using the systems and methods.

The invention claimed is:
 1. A system for detecting tremors comprising:a contactless tremor detector configured to be positioned in proximityto a body part of an individual without touching the body part, thetremor detector including an oscillator circuit having a sensing coil,an oscillator configured to generate alternating electromagnetic fields,and a frequency counter configured to sense a resonant frequency of theoscillator circuit as the resonant frequency changes in response totremors of the body part of the individual adjacent to the sensing coil;and a computing device configured to: receive resonant frequency datafrom the contactless tremor detector, convert the resonant frequencydata into distance data, and convert the distance data into frequencydata that identifies a frequency at which the body part tremors.
 2. Thesystem of claim 1, wherein the oscillator and the frequency counter areintegrated together as an induction sensor.
 3. The system of claim 2,wherein the induction sensor is configured to convert the resonantfrequencies into digital resonant frequency data.
 4. The system of claim2, wherein the induction sensor comprises an induction sensing chip. 5.The system of claim 1, wherein the tremor detector further includes amicrocontroller configured to receive resonant frequency data sensed bythe frequency counter.
 6. The system of claim 1, wherein the computingdevice is configured to convert the resonant frequency data intodistance data using a correlation graph or table generated during acalibration process.
 7. The system of claim 1, wherein the computingdevice is configured to convert the distance data into movementfrequency data by performing a Fourier transform on the distance data.8. A system for detecting tremors comprising: a contactless tremordetector configured to be positioned in proximity to a body part of anindividual without touching the body part, the tremor detector includinga sensing coil, an oscillator in electrical communication with thesensing coil configured to deliver a signal to the sensing coil thatcauses the sensing coil to generate alternating electromagnetic fieldsthat create an eddy current on a surface of the body part placed inproximity to the sensing coil, wherein magnetic fields generated by theeddy current couple back to the sensing coil and change a resonantfrequency of the oscillator, a frequency counter configured to sense theresonant frequency of the oscillator as the resonant frequency changesin response to tremors of the body part, and a microcontrollerconfigured to receive resonant frequency data from the oscillatorcircuit, and a computing device configured to receive the resonantfrequency data from the microcontroller, convert the resonant frequencydata into distance data that indicates a distance of the body part fromthe sensing coil as a function of time, and convert the distance datainto movement frequency data that identifies a frequency at which thebody part tremors.
 9. The system of claim 8, wherein the computingdevice is configured to convert the resonant frequency data intodistance data using a correlation graph or table generated during acalibration process.
 10. The system of claim 8, wherein the computingdevice is configured to convert the distance data into movementfrequency data by performing a Fourier transform on the distance data.